Final Project (part 1)

Let’s prepara our data and plots. We should also save the date before the final plot.

# clean memory
rm(list = ls()) 
# link
link='eduwa.rda'

#getting the data TABLE from the file:
load(file=link)
eduwa=eduwa[complete.cases(eduwa),]#no missing values

# saving statistics for the data to plot
statVals=summary(eduwa$Reduced.Lunch,digits = 3)[1:6]
statVals=as.vector(statVals) # must turn into a vector

# computing the upper threshold
theIQR=IQR(eduwa$Reduced.Lunch,na.rm = T)
upperT=summary(eduwa$Reduced.Lunch)[[5]] + theIQR*1.5

# computing amount of outliers
numOutliers=sum(eduwa$Reduced.Lunch>upperT,na.rm = T)

# texts to use in plot
txtUpper=paste0('Threshold:',upperT)
txtOut=paste0('Total outlying schools: ',numOutliers)

# I will color the "mean" (in statVals)
statCols=c("black","black","black","blue","black","black")

# THE BOXPLOT
library(ggplot2)
## base
base= ggplot(eduwa,aes(x=0,y = Reduced.Lunch)) + 
      theme_classic()  
## horizontal boxplot
b1= base + geom_boxplot() + coord_flip()
##  breaks in y-axis will be the 'statVals' (horizontal)
b1=b1+ scale_y_continuous(breaks = statVals) 
## customizing text in the x-axis (flipped)
b1=b1+theme(axis.text.x = element_text(angle = 60,
                                       face = 'bold',
                                       size = 7,
                                       vjust = 0.5)) + 
    ## coloring
    theme(axis.text.x=ggtext::element_markdown(colour=statCols)) +
    ## customizing text in the y-axis (flipped)
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank()) +
    ## plot caption
    theme(plot.caption = element_text(hjust=0))
## customizing titles
textForCaption="WA State official records\nNote: Relevant values for boxplot shown on axis.\nMean value in blue."
b1=b1 + labs(title = "Reduced lunch distribution",
             subtitle = "WA State (2023)",
             caption = textForCaption,
             y= "Count of Students benefitted")
## annotating
b1_ann = b1 + geom_hline(yintercept = upperT,
                         color='red',
                         linetype="dotted",
                         linewidth=1) 
b1_ann=b1_ann + annotate(geom = 'text',
                         size=3,
                         label=txtUpper,
                         y = upperT+5,
                         x=0.2,
                         angle=90) # text angle

BoxReducedLunch=b1_ann + annotate(geom = 'text',
                         size=3,
                         label=txtOut,
                         y = upperT+60,
                         x=0.1,
                         angle=0)
BoxReducedLunch

# Preparing Frequency Table
absoluteT=table(eduwa$LocaleType) 
propT=prop.table(absoluteT)*100
tableFreq=as.data.frame(absoluteT)
names(tableFreq)=c("Locale","Count")
tableFreq$Percent=as.vector(propT)

# order of locales by percent
tableFreq=tableFreq[order(tableFreq$Percent),]

# adding columns to frequency table for lollipop
tableFreq$gap=tableFreq$Percent-25
tableFreq$PositiveGap=ifelse(tableFreq$gap>0,"Yes","No")

# save data frame: tableFreq
write.csv(tableFreq,"tableFreq.csv",row.names = F)

# clean memory
rm(list = ls()) 
#read in data frame
tableFreq=read.csv("tableFreq.csv")

#THE LOLLIPOP
## base
base = ggplot(tableFreq, aes(x=Locale,
                             y=gap)) +
             theme_classic()

## order of sticks
base= base + scale_x_discrete(limits=tableFreq$Locale)

# making the sticks
lp1=base + geom_segment(aes(y = 0, yend = gap,
                            x = Locale,
                            xend = Locale),
                        color = "gray") 
# making the candy
lp2=lp1 + geom_point(aes(color=PositiveGap)) 

# adding text
lp3= lp2 + geom_text(aes(label = round(gap,1)),
                     nudge_x=0.15,#move to the right
                     show.legend = FALSE) 
# line at y=0
lp4 = lp3 + geom_hline(yintercept = 0)

# customizing axis: element_blank() means NOT TO SHOW
lp5 = lp4 + theme(axis.ticks.y = element_blank(),
                  axis.title.y = element_blank(),
                  axis.line.y = element_blank(),
                  axis.text.y = element_blank())
lp6=lp5 + theme(axis.ticks.x = element_blank(),
                axis.title.x = element_blank(),
                axis.line.x = element_blank(),
                axis.text.x = element_blank())
# labels for sticks
lp7 = lp6 +  geom_label(aes(label=Locale),
                        color ='black ',
                        size =3,
                        y=0,
                        show.legend = FALSE ) 
# customizing legend position
lp8= lp7 + theme(legend.position = c(0.8,0.3),
                 legend.background = element_rect(color='black'))

# labs
textTitle="Share of schools by Location"
textCapt="Source: WA Official Records.\nNote: Lines represent distance from 25%"
LolliSchoolsLocation=lp8 + labs(title = textTitle,
           subtitle = "WA State 2023",
           caption = textCapt,
           color="Above 25%?")
LolliSchoolsLocation

# clear memory
rm(list = ls())
# collecting the data
crimes=read.csv("SPD_Crime_Data__2008-Present.csv")
crimes=crimes[complete.cases(crimes),]
row.names(crimes)=NULL

#set the date column
library("lubridate")

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

stringDate=crimes$Offense.Start.DateTime
formatDate="%m/%d/%y %H:%M:%S %p" #also the time
crimes$Offense.Start.DateTime=parse_date_time(stringDate,formatDate)
#create columns with date information
crimes$year=year(crimes$Offense.Start.DateTime)
crimes$weekDay=wday(crimes$Offense.Start.DateTime,label = T)
crimes$dayHour=hour(crimes$Offense.Start.DateTime)


#subset
crimes2019_22=crimes[crimes$year>2018 & crimes$year<2023,]
rm(crimes)
#categorize daytime
dayTimeCat=c("Night", "Morning", "Afternoon", "Evening")
crimes2019_22$dayTime = cut(x=crimes2019_22$dayHour, 
                            breaks = c(0,6,12,18,23), 
                            labels = dayTimeCat,
                            include.lowest=TRUE)
#changing capitalization 
library(stringr)
#### first renaming column
names(crimes2019_22)=gsub("Offense.Parent.Group",#old names
                          "crimeName",#new names
                          names(crimes2019_22)) #all current names
# then...
crimes2019_22$crimeName=str_to_title(crimes2019_22$crimeName)

#two way table facet plot
CrimeDay=table(crimes2019_22$crimeName,
               crimes2019_22$dayTime)
#making a data frame from contingency table
CrimeDayDF=as.data.frame(CrimeDay)
CrimeDay_mgCol=prop.table(CrimeDay,margin = 2) #marginal
names(CrimeDayDF)=c("crime","daytime","counts")#renaming
#adding marginal
CrimeDayDF$share=as.data.frame(CrimeDay_mgCol)[,3]
CrimeDayDF$share=round(100*CrimeDayDF$share,1)
# save table
write.csv(CrimeDayDF,"CrimebyDaytime_2019_22.csv",row.names=F)
saveRDS(crimes2019_22,'crimes2019_22.rds')

rm(list = ls())
CrimeDayDF=read.csv("CrimebyDaytime_2019_22.csv")

# new base
base  = ggplot(CrimeDayDF, 
               aes(x = reorder(crime, share), 
                   y = share ) ) + theme_minimal()
#bars
bars = base + geom_bar(stat = "identity" ) 

#bars facetted and flipped
barsFct = bars + facet_grid( ~ daytime)  + coord_flip() 

# customizing y axis
barsFct=barsFct + theme(axis.text.y = element_text(size=7,
                                                   angle = 20)) 
# customizing text in bars
barsFct=barsFct + geom_text(aes(label=ifelse(share>9,
                                             share,
                                             "")),
                            nudge_y = 5,
                            size=2.5) 
# labs
textCaption="Source: Seattle Open Data Portal\nNote: Annotations if >=10%"
barsCrimeDay=barsFct + labs(title = "Crimes by daytime",
                       subtitle = "Seattle - WA, 2019-2022",
                       x=" ", y= "Percent (%)",
                       caption = textCaption)

barsCrimeDay

# clear memory
rm(list = ls())
crimes=readRDS('crimes2019_22.rds')

#saving TOP 4 crimes
topCrimes=names(tail(sort(table(crimes$crimeName)),4))

#recoding crimes in a new variable
crimes$crimeMini=ifelse(crimes$crimeName %in%topCrimes,crimes$crimeName,"Other")
#recoding the new variable
crimes$crimeMini=gsub("Assault Offenses","Asault",crimes$crimeMini)
crimes$crimeMini=gsub("Burglary/Breaking&Entering","Burglary",crimes$crimeMini)
crimes$crimeMini=gsub("Destruction/Damage/Vandalism Of Property","Vandalism",crimes$crimeMini)
crimes$crimeMini=gsub("Larceny-Theft","Theft",crimes$crimeMini)

#no missing
crimes=crimes[complete.cases(crimes),]

#create column year-month
library(lubridate)
crimes$year_week <- floor_date(crimes$Offense.Start.DateTime,"week")


saveRDS(crimes,'crimes2019_22.rds')

# prepare count per month-year
crimeDate=as.data.frame(table(crimes$year_week))  # date will be a factor
names(crimeDate)=c("date",'count')
#formatting column in Freq Table:
crimeDate$date=as.Date(crimeDate$date)
saveRDS(crimeDate,"crimeWeeklyCount.rds") # to keep date format

# clear memory
rm(list = ls())
library(ggplot2)
crimeDate=readRDS("crimeWeeklyCount.rds")
base=ggplot(crimeDate,
            aes(x=date,y=count))
lines=base  + geom_line(alpha=0.3) + stat_smooth(color = "red",
                      method = "loess") 
#labs
captionText="Source: Seattle Open Data Portal\nNote:Trend computed using loess algorithm"

Line_crimeTime=lines + labs(title = "Crimes in Seattle-WA",
                            subtitle = "Weekly count, 2019-2022",
                            caption = captionText)
Line_crimeTime

# clean memory
rm(list = ls()) 

# location of the data
link="safeCitiesIndexAll.xlsx"

# 'rio' can be used to import EXCEL files:
library(rio)
safe=import(link)

library(magrittr)
safe$DIN=apply(safe[,c(grep("D_In_",names(safe) ))],1,mean)%>%round(2)
safe$DOUT=apply(safe[,c(grep("D_Out_",names(safe) ))],1,mean)%>%round(2)

safe$HIN=apply(safe[,c(grep("H_In_",names(safe) ))],1,mean)%>%round(2)

safe$HOUT=apply(safe[,c(grep("H_Out_", names(safe) ))],1,mean)%>%round(2)

safe$IIN=apply(safe[,c(grep("I_In_", names(safe) ))],1,mean)%>%round(2)

safe$IOUT=apply(safe[,c(grep("I_Out_", names(safe) ))],1,mean)%>%round(2)

safe$PIN=apply(safe[,c(grep("P_In_", names(safe) ))],1,mean)%>%round(2)

safe$POUT=apply(safe[,c(grep("P_Out_", names(safe) ))],1,mean)%>%round(2)

safeINS=safe[,c(1,grep("IN$", colnames(safe)))] # '$' for 'end with'.

names(safeINS)=c("city",'Digital','Health','Infrastructure','Personal')
safeINS_long=reshape2::melt(safeINS,id.vars = 'city')

write.csv(safeINS_long,"safeINS_long.csv")

# clean memory
rm(list = ls()) 
safeINS_long=read.csv("safeINS_long.csv")
base = ggplot(data=safeINS_long, 
       aes(x=reorder(city, value,median),
           y=value)) + theme_classic()
point=base+geom_point(shape=5)
lolli=point+geom_segment(aes(x=city,xend = city,
                             y=0,yend = value),
                         color='grey',linewidth=0.2)
lolliFacet=lolli+facet_grid(~variable) + coord_flip()
lolliSafetyCity= lolliFacet+ theme(axis.text.y = element_text(size = 5)) +
                        labs(title = "Safety in cities",
                             subtitle = "(Scores on Measures taken)",
                             caption = "Source: The Economist",
                             y="Percent(%)",
                             x="")
lolliSafetyCity

# clean memory
rm(list = ls()) 

# location of the data
link="safeCitiesIndexAll.xlsx"

# 'rio' can be used to import EXCEL files:
library(rio)
safe=import(link)

allIN=safe[,c(grep("_In_", names(safe) ))]
allIN$city=safe$city
dist_in_safe=dist(allIN[,-24])

processResults= cluster::pam(x=dist_in_safe,
              k = 3, cluster.only = F)

#add to dataframe
allIN$cluster=processResults$clustering

theMap=cmdscale(dist_in_safe,k = 2)

allIN$dim1=theMap[,1]
allIN$dim2=theMap[,2]

write.csv(allIN,'allIN.csv')

# clean memory
rm(list = ls()) 

allIN=read.csv('allIN.csv')
library(ggrepel)
base=ggplot(allIN,aes(x=dim1,y=dim2,
                       label = city)) + theme_void() 
ps=base + geom_point(aes(color=as.factor(cluster)))
ps=ps +
               geom_text_repel(size=1.5,
                               max.overlaps = 20) 

#labs
textCaption="Source:The Economist\nNote: Clustering process followed k-medoids technique."
textLegend="Clusters\n(labels do not\nrepresent order)"
ps=ps + labs(title = "Safety of cities",
                     subtitle = "Scores on interventions (2023)",
                     caption=textCaption,
                     color=textLegend)

PointCitiesCluster=ps + theme(legend.background = element_rect(color='grey90'),
               legend.title = element_text(size = 6,hjust = 0),
               legend.position=c(0.2,0.05),
               legend.direction = "horizontal")
PointCitiesCluster

# clean memory
rm(list = ls()) 
crimes=readRDS("crimes2019_22.rds")
#names(crimes)
ToKeep=c("year",'crimeMini',"MCPP","Longitude","Latitude"  )
crimeTimePlace=crimes[,ToKeep]
rm(crimes) # remove from memory

#table(crimeTimePlace$MCPP) # check bad values, then correct
crimeTimePlace$MCPP=ifelse(crimeTimePlace$MCPP=='CAPTIOL HILL',
                           "CAPITOL HILL", #yes
                           crimeTimePlace$MCPP) #no
crimeTimePlace$MCPP=ifelse(crimeTimePlace$MCPP%in%c('UNKNOWN',"<Null>"),
                           NA,
                           crimeTimePlace$MCPP)
# get rid of NA
crimeTimePlace=crimeTimePlace[complete.cases(crimeTimePlace),]

saveRDS(crimeTimePlace,'crimeTimePlace2019_22.rds')

# counting crimes per year and neighborhood
crimeYear=as.data.frame(table(crimeTimePlace$MCPP,crimeTimePlace$year))
names(crimeYear)=c("NEIGHBORHOOD",'year','count')
# making sure it is a text:
crimeYear$NEIGHBORHOOD=as.character(crimeYear$NEIGHBORHOOD)

library(sf)
NeighborhoodMap=read_sf("Micro_Community_Policing_Plans.geojson")

crimeYear[crimeYear$NEIGHBORHOOD=="MADRONA/LESCHI",'NEIGHBORHOOD']="LESCHI/MADRONA"
crimeYear[crimeYear$NEIGHBORHOOD=="CHINATOWN/INTERNATIONAL DISTRICT",'NEIGHBORHOOD']="INTERNATIONAL DISTRICT"

# adding info
NeighborhoodMap=merge(NeighborhoodMap,crimeYear)
# saving
st_write(NeighborhoodMap, "NeighborhoodMap_crimecount.geojson",delete_dsn = T)

#coordinates as spatial elements
crimes_dot_map = st_as_sf(crimeTimePlace, 
                         coords = c("Longitude","Latitude"),
                         crs = st_crs(NeighborhoodMap)) # shared
boundsMap=st_bbox(NeighborhoodMap)%>% st_as_sfc()

crimes_dot_map=st_intersection(crimes_dot_map,
                               boundsMap)
#who are the top 4?
top_4=names(head(sort(table(crimes_dot_map$MCPP),decreasing = T),4))

#saving all
st_write(crimes_dot_map, "crimes_dot_map.geojson",delete_dsn = T)

#saving just top 4 without Other
str(crimes_dot_map$MCPP)
crimes_dot_map_allTop4=crimes_dot_map[crimes_dot_map$MCPP%in%top_4,]
crimes_dot_map_allTop4=crimes_dot_map_allTop4[crimes_dot_map_allTop4$crimeMini!='Other',]
st_write(crimes_dot_map_allTop4, "crimes_dot_map_allTop4.geojson",delete_dsn = T)

# saving each top
top1=crimes_dot_map_allTop4[crimes_dot_map_allTop4$MCPP==top_4[1],]
st_write(top1, "crimes_dot_map_top1.geojson",delete_dsn = T)

# saving each top
top2=crimes_dot_map_allTop4[crimes_dot_map_allTop4$MCPP==top_4[2],]
st_write(top2, "crimes_dot_map_top2.geojson",delete_dsn = T)

# saving each top
top3=crimes_dot_map_allTop4[crimes_dot_map_allTop4$MCPP==top_4[3],]
st_write(top3, "crimes_dot_map_top3.geojson",delete_dsn = T)

# saving each top
top4=crimes_dot_map_allTop4[crimes_dot_map_allTop4$MCPP==top_4[4],]
st_write(top4, "crimes_dot_map_top4.geojson",delete_dsn = T)

# clean memory
rm(list = ls()) 
library(sf)
MapCount=read_sf("NeighborhoodMap_crimecount.geojson")

library(ggplot2)
base=ggplot(data=MapCount) + theme_void()
mapYears=base + geom_sf(aes(fill=count),
                             color=NA)
mapYears=mapYears + facet_grid(~year) 
mapYears=mapYears + scale_fill_gradient(low = 'yellow',
                                 high= 'blue')
mapYears=mapYears + labs(title = "Crime events in Seattle by year",
                         caption="Source: Seattle Open Data Portal\nand Seattle Police Department.")
mapYears

# clean memory
rm(list = ls()) 
library(sf)
PointLocations=read_sf("crimes_dot_map_allTop4.geojson")



library(ggplot2)
base=ggplot(data=PointLocations) + theme_void()
aMap=base + geom_sf(aes(color=MCPP),size=1, alpha=0.2)
aMap=aMap+facet_grid(crimeMini~year) 

#labs
textTitle="Crime events in Seattle by year and type"
textCapt="Source: Seattle Open Data Portal\nand Seattle Police Department."
aMap=aMap + labs(title = textTitle,
                   subtitle = "Showing the Top 4 Neighborhoods",
                   caption=textCapt)
mapCrimeYears=aMap + theme(strip.text.y = element_text(angle = -90))

#alpha for legend symbols
hereMap= mapCrimeYears + 
         guides(colour = guide_legend(override.aes = 
                                      list(alpha = 1))) 
# reposition title and subtitle :
hereMap= hereMap + 
      theme(plot.title = element_text(vjust = 3,hjust=0.5), 
            plot.subtitle = element_text(vjust = 4,hjust = 0.5))

# reposition legend, and more
hereMap= hereMap + theme(
            legend.position=c(-0.7,0.3),
            #space between text and symbol in legend
            legend.spacing.x = unit(0,units = 'cm'),
            legend.text = element_text(size=6)) #text size
hereMap

rm(list = ls()) 
library(leaflet)
library(sf)
MapCount=read_sf("NeighborhoodMap_crimecount.geojson")


# color segun valor
MapCount2019=MapCount[MapCount$year==2019,]
paletteFun=colorQuantile("YlGnBu", 
                         MapCount2019$count,
                         n = 5)
# the base map
base= leaflet() %>% addTiles()
final2019 = base %>% 
         addPolygons(data=MapCount2019,
                     weight = 0, #width border
                     opacity =  1, # 0 transparencia total
                     fillOpacity = 0.8, # contraste de paleta
                     fillColor = ~paletteFun(count)) # coloreando

# color segun valor
MapCount2020=MapCount[MapCount$year==2020,]
paletteFun=colorQuantile("YlGnBu", 
                         MapCount2020$count,
                         n = 5)
# the base map
base= leaflet() %>% addTiles()
final2020 = base %>% 
         addPolygons(data=MapCount2020,
                     weight = 0, #width border
                     opacity =  1, # 0 transparencia total
                     fillOpacity = 0.8, # contraste de paleta
                     fillColor = ~paletteFun(count)) # coloreando

# color segun valor
MapCount2021=MapCount[MapCount$year==2021,]
paletteFun=colorQuantile("YlGnBu", 
                         MapCount2021$count,
                         n = 5)
# the base map
base= leaflet() %>% addTiles()
final2021 = base %>% 
         addPolygons(data=MapCount2021,
                     weight = 0, #width border
                     opacity =  1, # 0 transparencia total
                     fillOpacity = 0.8, # contraste de paleta
                     fillColor = ~paletteFun(count)) # coloreando

# color segun valor
MapCount2022=MapCount[MapCount$year==2022,]
paletteFun=colorQuantile("YlGnBu", 
                         MapCount2022$count,
                         n = 5)
# the base map
base= leaflet() %>% addTiles()
final2022 = base %>% 
         addPolygons(data=MapCount2022,
                     weight = 0, #width border
                     opacity =  1, # 0 transparencia total
                     fillOpacity = 0.8, # contraste de paleta
                     fillColor = ~paletteFun(count)) # coloreando

saveRDS(final2019,"final2019.rds")
saveRDS(final2020,"final2020.rds")
saveRDS(final2021,"final2021.rds")
saveRDS(final2022,"final2022.rds")

rm(list = ls()) 
library(leaflet)
library(manipulateWidget)
combineWidgets(readRDS("final2019.rds"),
     readRDS("final2020.rds"),
     readRDS("final2021.rds"),
     readRDS("final2022.rds"))

rm(list = ls()) 
library(leaflet)
library(sf)

PointLocations=read_sf("crimes_dot_map_top1.geojson")

library(htmltools)

title2019 <- tags$p(tags$style("p {color: red; font-size:12px}"),
            tags$b("2019"))
title2020 <- tags$p(tags$style("p {color: red; font-size:12px}"),
            tags$b("2020"))
title2021 <- tags$p(tags$style("p {color: red; font-size:12px}"),
            tags$b("2021"))
title2022 <- tags$p(tags$style("p {color: red; font-size:12px}"),
            tags$b("2022"))

###
CrimeVector=c("Asault", "Burglary","Theft","Vandalism")
Ps19=PointLocations[PointLocations$year==2019,]

final2019p=leaflet() %>% addTiles() %>% 
    addCircleMarkers(data=Ps19[Ps19$crimeMini=='Asault',], radius=1,
               group = "Asault")%>% 
    addCircleMarkers(data = Ps19[Ps19$crimeMini=='Burglary',],radius=1,
               group = "Burglary")%>% 
    addCircleMarkers(data = Ps19[Ps19$crimeMini=='Theft',],radius=1,
               group = "Theft")%>%
    addCircleMarkers(data = Ps19[Ps19$crimeMini=='Vandalism',],radius=1,
               group = "Vandalism")%>%
    addLayersControl(
        baseGroups = CrimeVector,
    options = layersControlOptions(collapsed = FALSE)
  )%>%
addControl(title2019, position = "bottomright" )



Ps20=PointLocations[PointLocations$year==2020,]

final2020p=leaflet() %>% addTiles() %>% 
    addCircleMarkers(data=Ps20[Ps20$crimeMini=='Asault',], radius=1,
               group = "Asault")%>% 
    addCircleMarkers(data = Ps20[Ps20$crimeMini=='Burglary',],radius=1,
               group = "Burglary")%>% 
    addCircleMarkers(data = Ps20[Ps20$crimeMini=='Theft',],radius=1,
               group = "Theft")%>%
    addCircleMarkers(data = Ps20[Ps20$crimeMini=='Vandalism',],radius=1,
               group = "Vandalism")%>%
    addLayersControl(
        baseGroups = CrimeVector,
    options = layersControlOptions(collapsed = FALSE)
  )%>%
addControl(title2020, position = "bottomright" )
##


Ps21=PointLocations[PointLocations$year==2021,]

final2021p=leaflet() %>% addTiles() %>% 
    addCircleMarkers(data=Ps21[Ps21$crimeMini=='Asault',], radius=1,
               group = "Asault")%>% 
    addCircleMarkers(data = Ps21[Ps21$crimeMini=='Burglary',],radius=1,
               group = "Burglary")%>% 
    addCircleMarkers(data = Ps21[Ps21$crimeMini=='Theft',],radius=1,
               group = "Theft")%>%
    addCircleMarkers(data = Ps21[Ps21$crimeMini=='Vandalism',],radius=1,
               group = "Vandalism")%>%
    addLayersControl(
        baseGroups = CrimeVector,
    options = layersControlOptions(collapsed = FALSE)
  )%>%
addControl(title2021, position = "bottomright" )
###


Ps22=PointLocations[PointLocations$year==2022,]

final2022p=leaflet() %>% addTiles() %>% 
    addCircleMarkers(data=Ps22[Ps22$crimeMini=='Asault',], radius=1,
               group = "Asault")%>% 
    addCircleMarkers(data = Ps22[Ps22$crimeMini=='Burglary',],radius=1,
               group = "Burglary")%>% 
    addCircleMarkers(data = Ps22[Ps22$crimeMini=='Theft',],radius=1,
               group = "Theft")%>%
    addCircleMarkers(data = Ps22[Ps22$crimeMini=='Vandalism',],radius=1,
               group = "Vandalism")%>%
    addLayersControl(
        baseGroups = CrimeVector,
    options = layersControlOptions(collapsed = FALSE)
  )%>%
addControl(title2022, position = "bottomright" )

saveRDS(final2019p,"final2019p.rds")
saveRDS(final2020p,"final2020p.rds")
saveRDS(final2021p,"final2021p.rds")
saveRDS(final2022p,"final2022p.rds")

rm(list = ls()) 
library(leaflet)
library(leaflet.minicharts)
library(manipulateWidget)
library(mapview)
combineWidgets(readRDS("final2019p.rds")%>%
    syncWith("maps"),
     readRDS("final2020p.rds")%>%
    syncWith("maps"),
    
     readRDS("final2021p.rds")%>%
    syncWith("maps"),
    
     readRDS("final2022p.rds")%>%
    syncWith("maps"))